Tag Archives: DevOps
So I See You Want To CI/CD

Avoiding Common Pitfalls When Getting Started With DevOps

If you’re in the planning or early development stages of implementing CI/CD for the first time, this post might help you.

DevOps is all the rage. It’s the new fad in tech! Years ago we were saying we should rely less on manual testing and fold testing into our engineering process. Now we are saying we should rely less on manual deployments and fold deployments and operational support into our engineering process. This all sounds lovely to me!

Having been a part of this effort toward automating more and more of our engineering process for the bulk of my career, I’ve had the opportunity to see CI/CD initiatives go awry. Strangely, it’s not self-evident how to setup a CI/CD pipeline well. It’s almost as if translating theory into practice is where the work is.

There are several inter-related subject-areas that need to be aligned to make a CI/CD pipeline successful. They are:

Let’s talk about each of them in turn.

Source Control

Your code is in source control right? I hate to ask, but I’m surprised by how often I have encountered code that is not in source control. A common answer I get is “yes, except for these 25 scripts we use to perform this or that task.” That’s a “no.” All of your code needs to be in source control. If you’re not sure where to put those scripts, create a /scripts folder in your repo and put them there. Get them in there, track changes, and make sure everyone is using the same version.

It’s customary for the repo structure to look something like

/src
/build
/docs
/scripts
/tests
README.md
LICENSE.md

I also encourage you to consider adopting a 1 repo, 1 root project, 1 releasable component standard separating your repositories. Releasable components should be independently releasable and have a separate lifecycle from other releasable components.

Branching Strategy

You should use a known, well-documented branching strategy. The goal of a branching strategy is to make sure everyone knows how code is supposed to flow through your source control system from initial development to the production release. There are three common choices:

Feature Branch Git Flow Commit to Master
  • Feature branches are taken from master.
  • Features are developed and released separately. They are merged to master at release time.
  • Appropriate for smaller teams who work on one feature at a time.
  • Continuous Integration happens on the feature branch.
  • More sophisticated version of feature branching.
  • Allows multiple teams to work in the project simultaneously while maintaining control over what gets released.
  • Appropriate for larger teams or where simultaneous feature development is needed.
  • Continuous Integration happens on the develop branch.
  • For mature DevOps teams.
  • Use feature flags to control what code is active in production.
  • Requires lifecycle management of feature flags.
  • Continuous Integration happens on master.

Some purists will argue that Continuous Integration isn’t happening unless you’re doing Commit to Master. I don’t agree with this. My take is that as long as the team is actively and often merging to the same branch, then the goal of Continuous Integration is being met.

Automated Build

Regardless of what programming language you are using, you need an automated build. When your build is automated, your build scripts become a living document that removes any doubt about what is required to build your software. You will need an automated build system such as Jenkins, Azure DevOps, or Octopus Deploy. You need a separate server that knows how to run your build scripts and produce a build artifact. It should also programmatically execute any quality gates you may have such as credentials scanning or automated testing. Ideally, any scripts required to build your application should be in your repo under the /build folder. Having your build scripts in source control has the additional advantage that you can use and test them locally.

Automated Testing

Once you can successfully build your software consistently on an external server (external from your development workstation), you should add some quality gates to your delivery pipeline. The first, easiest, and least-expensive quality gate should be unit tests. If you have not embraced Test Driven Development, do so. If your Continuous Integration server supports it, have it verify that your software builds and passes your automated tests at the pre-commit stage. This will prevent commits from making it into your repo if they don’t meet minimum standards. If your CI server does not support this feature, make sure repairing any failed builds or failed automated testing is understood to be the #1 priority of the team should they go red.

Build Artifacts

Once the software builds successfully and passes the initial quality gates, your build should produce an artifact. Examples of build artifacts include nuget packages, maven packages, zip files, rpm files, or any other standard, recognized package format.

Build artifacts should have the following characteristics:

  • Completeness. The build artifact should contain everything necessary to deploy the software. Even if only a single component changed, the artifact should be treated like it is being deployed to a fresh environment.
  • Environment Agnosticism. The build artifact should not contain any information specific to any environment in which it is to be deployed. This can include URL’s, connection strings, IP Addresses, environment names, or anything else that is only valid in a single environment. I’ll write more about this in Environment Segregation.
  • Versioned. The build artifact should carry it’s version number. Most standard package formats include the version in the package filename. Some carry it as metadata within the package. Follow whatever conventions are normally used for your package management solution. If it’s possible to stamp the files contained in the package with the version as well (e.g., .NET Assemblies), do so. If you’re using a zip file, include the version in the zip filename. If you are releasing a library, follow Semantic Versioning. If not, consider versioning your application using release date information (e.g., for a release started on August 15th, 2018 consider setting the version number to 2018.8.15 or 1808).
  • Singleton. Build artifacts should be built only once. This ensures that the artifact you deploy to your test environment will be the artifact that you tested when you go to production.

Deployment Automation

Your deployment process should be fully automated. Ideally, your deployment automation tools will simply execute scripts they find in your repo. This is ideal because it allows you to version and branch your deployment process along with your code. If you build your release scripts in your release automation tool, you will have integration errors when you need to modify your deployment automation for different branches independently.

The output of your build process is a build artifact. This build artifact is the input to your deployment automation along with configuration data appropriate to the environment you are deploying to.

Taking the time to script your deployment has the same benefits as scripting your build–it creates a living document detailing exactly how your software must be deployed. Your scripts should assume a clean machine with minimal dependencies pre-installed and should be re-runnable without error.

Take advantage of the fact that you are versioning your build artifact. If you are deploying a website to IIS, create a new physical directory matching the package and version name. After extracting the files to this new directory, repoint the virtual directory to the new location. This makes reverting to the previous version easy should it be necessary as all of the files for the previous version are still on the machine. The same trick can be accomplished on Unix-y systems using sym-links.

Lastly, your deployment automation scripts are code. Like any other code, it should be stored in source control and tested.

Environment Segregation

I’ve written that you should avoid including any environment-specific configuration in your build artifact (and by extension, in source control), and I’ve said that you should fully automate your deployment process. The configuration data for the target environment should be defined in your deployment automation tooling.

The goal here is to use the same deployment automation regardless of which environment you are deploying to. That means there should be no special steps for special environments.

Most deployment automation tools support some sort of variable substitution for config files. This allows you to keep the config files in source control with defined placeholders where the environment-specific configuration would be. At deployment time, the deployment automation tools will replace the tokens in the config files with values that are meaningful for that environment.

If variable substitution is not an option, consider maintaining a parameter-driven build script that writes out all your config files from scratch. In this case your config files will not be in source control at all but your scripts will know how to generate them.

The end-result of all of this is that you should be able to select any version of your build, point it to the environment of your choice, click “deploy,” and have a working piece of software.

Epilogue

The above is not a complete picture of everything you need to consider when moving towards DevOps. I did not cover concepts such as post-deployment testing, logging & monitoring, security, password & certificate rotation, controlling access to production, or any number of other related topics. I did however cover things you should consider when getting started in CI/CD. I’ve seen many teams attempt to embrace DevOps and create toil for themselves because they didn’t understand the material I’ve covered here. Following this advice should save you the effort of making these mistakes and give you breathing room to make new ones :).

10 Things I Wish I Had Known Before I Switched to DevOps

1. DevOps is hard

It might not seem like it, but DevOps is hard. A few years ago I thought to myself that it can’t be that difficult since installing an individual application isn’t that difficult. I was wrong in part because…

2. Security is hard

Production is scary. I’d rather not have access when possible. On the other hand the tools that we use will definitely need access to production since it’s kind of the reason they exist. This means we have to have very tight control over who has access to the credentials that the tools run under. We work to limit our own day-to-day accounts so that their access is limited as well.

As a developer I didn’t think much about Security. I pretty much just stuffed an AD Group in a config file somewhere when I was told to and I was done. As a DevOps engineer I have had (and will continue) to learn a lot more about security and its organization even though I don’t manage security for my organization. Security impacts deployments at every level so you will have to learn about security infrastructure in order to make safe and practical recommendations to your security administration group.

3. You are not Netflix (unless you are)

Our organization got excited about DevOps tools after seeing some compelling presentations by Netflix at QCon San Francisco. Netflix has the need for highly scaled web servers which fully embrace the “cattle vs. pets” philosophy because they have millions of concurrent users of a publicly facing service.

We are not Netflix. We have 50+ internal applications with usage rates measured in the 10’s. They’re important to us–they run our business–but our problems are not the same ones Netflix faces. The tools that Netflix uses are designed to solve problems Netflix has. That doesn’t necessarily make them a good fit for our needs. We lost a lot of time and effort trying to make Netflix solutions fit our problems.

4. Windows vs. Linux matters when choosing your tools.

There are basically 5 possibilities when it comes to your server topology:

  1. Windows Only
  2. Linux Only
  3. Windows Dominant
  4. Linux Dominant
  5. Hetergeneous

If you are managing a homogeneous ecosystem then it’s imperative that you use tools that natively support that system. Don’t try to use Linux tools to manage Windows and vice versa. If you do, you’re gonna have a bad time. If you are primarily deploying to Windows you should look at tools like Octopus Deploy or Build Master. If you’re managing a Linux ecosystem look into Chef, Puppet, or even Docker.

If you’re managing a mixed ecosystem where one OS was dominant, you should still use tools designed to support the dominant system. It may be worth the effort to see if your existing tools can also manage the subdominant system. In our case it’s not worth the effort so we have instead moved toward an “appliance” model for our Linux servers. What this means is instead of managing a bunch of code to deploy RabbitMQ to Linux, we’re instead creating VM Images for the Rabbit installation which we can hydrate at will. We have far fewer resources who know how to administer Linux so this model works better for us.

5. DevOps tools are in their infancy

DevOps tools are optimized for the problems their creators were facing. There are many more problems in the DevOps space than any of the dominant tools are capable of managing on their own.

For example, Chef wants to deploy a machine. It’s not primarily concerned about applications. The Chef model is to declare the state of the machine and then let Chef decide how to bring the machine to that state. This approach optimizes for horizontally scaling hundreds or thousands of identical nodes with very few commands. Awesome!

In our organization we see the world in terms of Applications–not machines. Our whole way of thinking about deployment is different than the way Chef looks at it. This isn’t a deficiency in Chef or in the way we look at the world, but when we started using Chef we weren’t aware of how fundamental that difference in perspective would actually be.

Because Chef looks at the world in terms of nodes, it has no built-in (or even recommended) solution for artifact and version management. We had to build that. We had to build solutions for managing cookbook versions, publishing artifact and cookbook versions into targeted environments, and forwarding changes to production to antecedent environments.

If you’re using Octopus (we’re migrating from Chef to Octopus) and looking at the world in terms of applications, you will have problems when you need to spin up new environments and whole machines with many applications pre-installed. Either way, you will have to build other tools to glue the off-the-shelf tools together.

(Aside: Though I am not personally a fan of Chef, I have heard of people using Chef to deploy their infrastructure and using Octopus to deploy applications.)

6. DevOps “best practices” are in their infancy

Chef likes to advertise “use Chef however you want! We’re flexible!” Great…. except Chef is complicated and I would like some guidance on how to use it! This isn’t so much a problem with Chef though–DevOps in general is a very young field so we don’t have the wealth of shared experience from which to draw generalized lessons. To the extent that there is guidance it’s basically cribbed from Software Engineering best practices and doesn’t always apply well.

Here are some of mine:

  1. Have a canary environment that rebuilds all machines and redeploys all software on a regularly scheduled basis. Use this environment to detect problems in your deployment tool chain early.
  2. Every developer should have an individual environment of their own to test deployments.
  3. Every team should have at least one environment for testing and/or UAT.
  4. Avoid “Standard Failures.” These are errors that occur often and either do not have a known solution or have a manual workaround. Identify the root cause of errors and address them. Incorporate manual workaround solutions into your automated solutions.
  5. Where possible, embed some sort of “health check” into your applications that you can invoke to have the application check it own configuration.
  6. Identify rollback strategies for your applications.

7. Developers will have to learn infrastructure

If you come from a development background you will have to learn about security, networking, hardware, virtual hardware, etc. This is the domain you are working in now. I’m still at the beginning of this process myself but I’m starting to see the size of how much I still have to learn. For example, if you’re deploying to the cloud you’ll have to learn the inner workings of your chosen cloud infrastructure.

8. Ops will have to learn development patterns and practices.

If you come from an Ops background you will have to learn Software Engineering patterns and practices. You are graduating from someone who writes the occasional script to someone who manages code. Writing some code that only has to be run once is easy. Writing code that has to work again and again and again as well as tolerate change is much, much harder. As the number of people, environments, and machines grow software engineering skills will become more and more important.

9. Don’t automate a bad process.

Consider this: Chef doesn’t provide a built-in way to define which artifacts should be deployed to which environments. To that end we built an “application versions” cookbook which contains a list of all applications, their version, and their artifact location. In order to start work a team must:

  1. Take a branch of the application versions cookbook.
  2. Edit the versions/artifact information.
  3. Upload the cookbook to Chef
  4. commit and push the changes back to github
  5. clone the chef-repo
  6. edit the affected environment to use the new version of the application versions cookbook.
  7. commit/push chef-repo
  8. upload the edited environment to Chef.

Does that sound like a good idea to you? It doesn’t to me–but it’s necessary if you’re going to use a Chef Cookbook as a source for environment application versions. Before you go and wrap some automation around this to make it “easier,” let’s challenge the basic assumption: should we maybe just store application versions by environment elsewhere? A json file on a network share would be easier than this.

When you automate a process (even to make it “easier”) you’re pouring a certain amount of lime over it. Be careful.

10. “Infrastructure as Code!” is not always a good idea.

Code != Artifacts != Configuration. The daily work of DevOps breaks down into basically three disciplines: Code, Configuration, and Artifact management. A change to one of these should not necessitate a change to the other. That means that Code, Configuration, and Artifacts should not live together in github.

Use a Package Manager for your artifacts. If you don’t know where to look check out Artifactory. It’s a versatile artifact repository that supports many different kinds of package managers. It’s API even understands version numbers and will let you identify and retrieve the “latest” version of your artifact. Let your CI server publish artifacts to your package manager and make it the canonical source for artifact retrieval.

Configuration should not be managed like code. Configuration data is any data required by applications to run. Examples are things like dns addresses, email addresses for notifications, database connection strings, api endpoints, etc.. Configuration data is just data about environments. Unlike code it does not need to be branched. It should be stored in some central repository and accessed directly by the deployment code.

The code that you use to execute your deployments is most emphatically and in every possible way code. This means it should be tested, stored in source control, subject to your company’s chosen branching strategies, built by a CI server, etc..

The “infrastructure as code” idea is a really great idea, but it applies only to the procedure of deploying hardware and software. It does not fit well with the metadata that describes which hardware and software should be deployed. Don’t use “infrastructure as code” as an execute to push square pegs into round holes.

Asynchrony in Powershell

As part of our Octopus Deploy migration effort we are writing a powershell module that we use to automatically bootstrap the Tentacle installation into Octopus. This involves maintaining metadata about machines and environments outside of Octopus. The reason we need this capability is to adhere to the “cattle vs. pets” approach to hardware. We want to be able to destroy and recreate our machines at will and have them show up again in Octopus ready to receive deployments.

Our initial implementation cycled through one machine at a time, installing Tentacle, registering it with Octopus (with the same security certificate so that Octopus recognizes it as the same machine), then moving on to the next machine. This is fine for small environments with few machines, but not awesome for larger environments with many machines. If it takes 2m to install Tentacle and I have 30 machines, I’m waiting an hour to be able to use the environment. With this problem in mind I decided to figure out how we could parallelize the boostrapping of machines in our Powershell module.

Start-Job

Start-Job is one of a family of Powershell functions created to support asynchrony. Other related functions are Get-Job, Wait-Job, Receive-Job, and Remove-Job. In it’s most basic form, Start-Job accepts a script block as a parameter and executes it on a background thread.

# executes "dir" on a background thread.
$job = Start-Job -ScriptBlock { dir } 

The job object returned by Start-Job gives you useful information such as the job id, name, and current state. You can run Get-Job to get a list of running jobs, Wait-Job to wait on one or more jobs to complete, Receive-Job to get the output of each job, and Remove-Job to delist jobs in the current Powershell session.

Complexity

If that’s all there was to it, I wouldn’t be writing this blog post. I’d just tweet the link to the Start-Jobs msdn page and call it done. My scenario is that I need to bootstrap machines using code defined in my Powershell module, but run those commands in a background process. I also need to collate and log the output of those processes as well as report on the succes/failure of each job.

When you call Start-Job in Powershell it creates a new session in which currently loaded modules are not automatically loaded. If you have your powershell module in the $PsModulePath you’re probably okay. However, there is a difference between the version of the module I’m currently working on and testing vs. the one I have on my machine for normal use.

Start-Job has an additional parameter for a script block used to initialize the new Powershell session prior to executing your background process. The difficulty is that while you can pass arguments to the background process script block, you cannot pass arguments to the initialization script. Here’s how you make it all work.

Setup Code


# Store the working module path in an environment variable so that the new powershell session can locate the correct version of the module. # The environment variable will not persist beyond the current powershell session so we don't have to worry about poluting our machine state. $env:OctobootModulePath = (get-module Octoboot).Path $init = { # When initializing the new session, use the -Force parameter in case a different version of the module is already loaded by a profile. import-module $env:OctobootModulePath -Force } # create a parameterized script block $scriptBlock = { Param( $computerName, $environment, $roles, $userName, $password, $apiKey, ) Install-Tentacle -computer $computerName ` -environment $environment ` -roles $roles ` -userName $userName -password $password ` -apiKey $apiKey } # I like to use an -Async switch on the controlling function. Debugging issues is easier in a synchronous context than in an async context. Making the async functionality optional is a win. if ($async) { $job = Start-Job ` -ScriptBlock $scriptBlock ` -InitializationScript $init ` -Name "Install Tentacle on $($computerName)" ` -ArgumentList @( $computerName, $environment, $roles $userName, $password, $apiKey) -Debug:$debug } else { Install-Tentacle -computer $computerName ` -environment $environment ` -roles $roles ` -userName $userName -password $password ` -apiKey $apiKey }

The above code is in a loop in the controlling powershell function. After I’ve kicked off all of the jobs I’m going to execute, I just need to wait on them to finish and collect their results.

Finalization Code


if ($async) { $jobs = get-job $jobs | Wait-Job | Receive-Job $jobs | foreach { $job = $_ write-host "$($job.Id) - $($job.Name) - $($job.State)" } $jobs | remove-job }

Since each individual job is now running in parallel, bootstrapping large environments doesn’t take much longer than bootstrapping smaller ones. The end result is that hour is now reduced to a few minutes.

Octopus Deploy – Variables & Variable Sets

DevOps is a relatively new space in the software engineering world. There are a smattering of tools to aid in the automation of application deployments, but precious little guidance with respect to patterns and practices for using the new tools. As a guy who loves leaning on principles this lack of attention to best practices leaves me feeling a bit uncomfortable. Since I’m leading a migration to Octopus Deploy, I thought I would share some of the decisions we’ve made.

This series of posts is an attempt to start a conversation about best practices. I want to be clear: We have not been applying these ideas long enough to know what all of the ramifications are. Your mileage may vary.

Posts in this series
1. Environments
2. Roles
3. Variables & Variable Sets

Variables & Variable Sets

Octopus Deploy allows you to modify your application’s configuration through the use of variables. You can define variables at the project level, or share variable values between projects through variable sets. If you have relatively little sharing of variables between projects you will likely prefer to create variables at the project level. My team manages over 50 different applications. Many of them are web services designed to support SOA. The net impact is that we have a lot of shared variables and for this reason we define variables exclusively through variable sets. This saves us time hunting for where a given variable is defined.

We use 2 kinds of variable sets
1. Global
2. Role based

Global variable sets define values that might be required across the company irrespective of any particular application, or that are more easily managed together. For example, we wish to capture metadata about environments. Octopus itself does not have a facility for tagging environments with arbitrary metadata. To satisfy this goal we created a variable set called “environment” in which we create variables to indicate values such as “owner” and “abbr”. We use these values to compose the values of other variables such as dns addresses or email addresses.

We also have some environments for which we do not create dns addresses for the sites. In these environments we need to install web applications with alternate ports. We keep a variable sets to define the port number we use for web applications in these environments since they must be unique across the web server.

The number of global variable sets should be as small as possible.

Role based variable sets are variables defined for the specific roles they target. If we have a role called heroes-iis we will also have a variable set called heroes-iis. Since we create roles on a per-deployed-application basis, this helps us keep roles, projects, and variable sets linked. If heroes-iis as web service end points, this variable set may be included in some other project that depends on those end points.

Naming Conventions

It is important to have naming conventions for your variables. I highly recommend prefixing all variables in a variable set with the name of the variable set to avoid potential naming collisions. For example, If I have a variable set called heroes-iis it will have variables with names like:

  • heroes-iis.application-pool.name
  • heroes-iis.application-pool.password

Define a Standard Structure for Similar Variable Sets

Once you get the rhythm of installing applications with Octopus, you will discover that similar kinds of applications have similar variable definition needs. You can save yourself a lot of time and Chrome tabs by establishing a variable set template that you use when creating a variable set for each kind of application you deploy. Here is our variable set template for web applications being deployed to iis:

Variable Set Name Segment Field Variable Name Notes
name-iis application-pool name name-iis.application-pool.name The name of the application pool
username name-iis.application-pool.username the username the application pool runs under
password name-iis.application-pool.password the password the application pool runs under
host name.host This corresponds to the site name as registered in IIS. It does not include the protocol (http://, https://). It should be blank if the site is being deployed into an environment without a dns entry.
site-name name.site-name This will be just the name of the web application in environments that do not have a dns entry. If the environment has a dns entry, it should resolve the host property.
site-root name.site-root This is the url root for the site. It should include the protocol (http://, https://) as well as the port, and any additional routing information.
endpoints endpoint-name name.endpoints.endpoint-name A web service may expose one or more endpoints. These should have unique names. Their values should be defined with reference to the host and port variables.
connection-strings cs-name name.connection-strings.cs-name The name of the connection string in the config file.

Scope

Octopus Deploy allows you to scope variables by environment, role, or channel (as of 3.3). The scoping rules are as follows:

(environment1 OR environment2 OR ...) AND (role1 OR role2 OR ...) AND (channel1 OR channel2 OR ...)

I recommend that you scope variable values as broadly as possible. Use composed variable values where you can to minimize the number of variable values you have to maintain. For example:


heroes-iis.connection-strings.heroes-db => "Server=#{environment.sql-server.url}; Database=#{heroes-db.database-name};" heroes-db.database-name => #{HEROES_}#{environment.name} environment.sql-server.url => http://sql-server.#{environment.name}.com

By using a composed variable value I don’t need to scope the connection string variable itself. Instead, I can confine scoping to environment.name and satisfy the resolution of all of the descendant variables. This minimizes the number of variables I have to actively maintain as new environments are created.

Octopus Deploy – Roles

DevOps is a relatively new space in the software engineering world. There are a smattering of tools to aid in the automation of application deployments, but precious little guidance with respect to patterns and practices for using the new tools. As a guy who loves leaning on principles this lack of attention to best practices leaves me feeling a bit uncomfortable. Since I’m leading a migration to Octopus Deploy, I thought I would share some of the decisions we’ve made.

This series of posts is an attempt to start a conversation about best practices. I want to be clear: We have not been applying these ideas long enough to know what all of the ramifications are. Your mileage may vary.

Posts in this series
1. Environments
2. Roles
3. Variables & Variable Sets

Roles

When you add machines into Octopus, you must specify environments and roles for that machine. For our purposes, environments were pretty easy to define. Roles however took some work. Here are the kinds of roles we defined.

Operating Systems

Example: windows, linux

This is pretty easy. We started with Linux and Windows for this type of role. I can see a day when we may need to additionally specify ubuntu-14 or 2k8-r2. In the meantime, YAGNI.

Environment Types

Example: dev, uat, integration, staging, prod, support

Our environment naming convention for developer environments is dev-{first initial}{last name}. For uat environments it’s uat-{team}. There is only one of each integration, staging, production, and support environments. There are certain variables that are defined consistently across all dev environments but may differ in uat environments. For this reason we are applying the environment type as a role across all machines in the relevant environments.

Commands

Example: hero-db.migrator

This is a standalone role. There will only be one machine in each environment that will have this role. It’s purpose is to execute commands on some resource in the enviornment that should not be run multiple times or concurrently. A good example of this is an Entity Framework database migration. We choose one machine in an environment that database migrations can be run from.

Applications

Example: webapp-iis, topshelf-service

Each deployable application has its own role. Not every application gets installed on every machine in an environment. We use the -iis affix for applications installed into IIS regardless of whether they’re sites or web services. We use the -service affix for Windows Services. We do this because we sometimes have a family of applications that have the same name but target a different kind of application.

Octopus Deploy – Environments

DevOps is a relatively new space in the software engineering world. There are a smattering of tools to aid in the automation of application deployments, but precious little guidance with respect to patterns and practices for using the new tools. As a guy who loves leaning on principles this lack of attention to best practices leaves me feeling a bit uncomfortable. Since I’m leading a migration to Octopus Deploy, I thought I would share some of the decisions we’ve made.

This series of posts is an attempt to start a conversation about best practices. I want to be clear: We have not been applying these ideas long enough to know what all of the ramifications are. Your mileage may vary.

Posts in this series
1. Environments
2. Roles
3. Variables & Variable Sets

Our Default Lifecycle

Before I begin, I should give you some background on our development ecosystem. Our Octopus Lifecycle looks like this:

dev => uat => integration => staging => prod => support

The Environments

Name Convention Purpose Notes
dev dev-{first initial}{last name} The primary purpose of these environments is to test the deployment tooling itself. We have 15 or so individual developer environments. Each developer gets their own environment with 2 servers (1 Linux, 1 Windows) and all of our 60 or so proprietary applications installed to it.
uat uat-{team} These environments are used by teams to test their work. We have 10 or so User Acceptance Testing environments. These are a little bit more fleshed out in terms of hardware. There are multiple web servers behind load balancers. The machines are beefier. These enviornments are usually owned by a single team, though they may sometimes be shared.
integration N/A Dress rehearsal by Development for releases The integration environment is much closer to production. When multiple teams are releasing their software during the same release window, integration gives us a rehearsal environment to make sure all of the work done by the various teams will work well together.
staging N/A Dress rehearsal by Support for releases Staging is exactly like integration except that it is not owned by the Development department. We have a team of people who are responsible for executing releases. This is their environment to verify that the steps development gave them will work.
prod N/A Business Use Prod is not managed by the deployment engineering team. We build the button that pushes to prod, but we do not push it.
support N/A Rehearsal environment for support solutions Support is a post-production environment that mirrors production. It allows support personnel to test and verify support tasks in a non-prod environment prior to running them in production.